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Review of Accounting Studies

Erschienen in:

Open Access 05.02.2021

Management forecasts of volatility

verfasst von: Atif Ellahie, Xiaoxia Peng

Erschienen in: Review of Accounting Studies | Ausgabe 2/2021

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Abstract

We examine the predictive information content of the management forecasts of stock return volatility (i.e., expected volatility) that are disclosed in annual reports. We find that expected volatility predicts near-term and longer-term stock return volatility and earnings volatility incremental to implied volatility, historical volatility, firm characteristics, and alternative measures of uncertainty. We also find that expected volatility reflects managers’ private information about their firms’ future investment activities, such as mergers and acquisitions and R&D intensity. Finally, we find that the predictive power of expected volatility shrinks when managers have stronger incentives to manage earnings. Overall, we provide novel evidence that management forecasts of volatility contain private information about future uncertainty that can help forecast volatility.

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SFAS 123 from 1996 to 2006, and SFAS 123R (currently known as ASC 718) since 2006 (Financial Accounting Standards Board (FASB) 1995, 2004).

The Securities and Exchange Commission provides implementation guidelines for this disclosure: “The staff believes that Company B should consider those future events that it reasonably concludes a marketplace participant would also consider in making the estimation. For example, if Company B has recently announced a merger with a company that would change its business risk in the future, then it should consider the impact of the merger in estimating the expected volatility if it reasonably believes a marketplace participant would also consider this event” (see SEC Staff Accounting Bulletin Topic 14).

Higher expected volatility increases the estimated value of granted employee stock options, which increases equity-based compensation expense and the perceived total executive compensation.

While our focus is on forecasting horizons of one year and beyond, we also study, in robustness tests, stock return volatility over horizons of one, three, and six months.

In robustness tests, we also measure future stock return volatility starting at the 10-K filing date.

To better understand how firms might combine these different information sources, we read disclosures by a subsample of 200 firms that mention “implied volatility” or “implied volatilities” in their 10-K. Consistent with the discretion allowed by the standard, we find that firms are relatively opaque about the methodology used to estimate expected volatility. We find that eight of the 200 firms do not disclose any information about the methodology. Of the remaining 192 firms, 10% disclose that they use only historical volatility, 15% disclose that they use only implied volatility, 65% disclose that they use a combination of historical volatility and implied volatility, and 10% disclose that they use peer information.

We collect implied volatility from the standardized option price files (stdopdYYYY) in OptionMetrics for 30-day call options. We choose the 30-day option maturity to collect IVOL since data coverage for longer-dated options declines drastically and since this is the most liquid and best populated maturity in OptionMetrics. Further, using a constant maturity enables cross-sectional comparisons. However, a potential limitation of using implied volatility from 30-day options is that it captures short-horizon expectations for volatility and its forecasting ability may decline over longer horizons. In robustness tests, we use IVOL based on 360-day and 720-day options, which are available for fewer firms.

In robustness tests, instead of measuring standard deviation of stock returns over the prior four years, we control for of individual lags of annual stock return volatility.

In robustness tests, we also use Fama and Macbeth (1973) annual cross-sectional regressions with industry fixed effects.

Based on overlapping observations, expected volatility collected using our algorithm has a Pearson (Spearman) correlation of 0.92 (0.94) with the Compustat variable (optvol), which underscores the quality of our data.

Firms can disclose expected volatility used to calculate stock compensation expense in the annual report (10-K) or in the annual proxy statement (DEF 14A) (Cadman et al. 2020). Disclosure in either the 10-K or the proxy statement may be incorporated by reference in the other filing. In our primary analysis, we focus on disclosure in 10-Ks only, because (1) 10-Ks are the main venue for this disclosure, and (2) the proxy statement only includes stock options granted to named executive officers and directors, while the 10-K covers broad-based employee stock option plans. Nevertheless, in robustness tests we use EVOL disclosed in proxy statements. We find that our sample increases by approximately 5%, and our main results are unchanged. We have also restricted our main analyses to using only Compustat data for EVOL and find similar results.

For approximately 2000 firm-year observations with extreme values of EVOL, we manually verify the data. In robustness tests, we have also trimmed at the top and bottom 1% level and find consistent results.

In robustness tests, we also use Fama-Macbeth regressions with industry fixed effects. We find that the estimated coefficients from pooled regressions and Fama-Macbeth regressions are very similar, which suggests that our results are not sensitive to the choice of regression methodology.

If we do not control for one-year-ahead stock return volatility, the coefficient on EVOL is even higher. The decline in the coefficient on EVOL after controlling for one-year-ahead volatility corroborates the Table 4 findings that EVOL predicts one-year-ahead stock return volatility.

We measure the idiosyncratic component as the natural logarithm of the standard deviation of residuals estimated from firm-level stock returns regressed on Fama-French-Carhart four-factor returns. We measure the common component as the natural logarithm of the standard deviation of predicted returns estimated using the same four-factor model, which would reflect firm-specific exposures to common factors. We replace the dependent variable in eq. (1) with either the one-year-ahead idiosyncratic volatility or the one-year-ahead common component of volatility.

In robustness tests, we find similar results if we measure standard deviation of earnings over the next four quarters or compute the standard deviation of quarterly changes scaled by assets, instead of earnings levels scaled by assets.

While we cannot include firm fixed effects in the logistic regressions, we use OLS regressions with firm fixed effects and find qualitatively similar results.

We also examine the information content of EVOL for current investments. Future performance should be affected by current investment decisions, and the implications of these decisions for future volatility might be reflected in EVOL. Consistent with our expectations, we find that EVOL is positively associated with M&A and R&D intensity during the current year (untabulated).

We have also considered the analyst risk rating measures from Lui et al. (2007, 2012). Their hand-collected data covers a limited set of firms over the 1999–2006 period. Hence we can only match their analyst risk rating data for a small fraction (6.7%) of our sample. In untabulated results, we re-estimate the models in columns 3 and 4 of Table 4, after controlling for analyst risk ratings in this subsample, and find that EVOL has incremental predictive power for volatility.

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Titel: Management forecasts of volatility
verfasst von: Atif Ellahie
Xiaoxia Peng
Publikationsdatum: 05.02.2021
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 2/2021
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-020-09567-4

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